Vol. 4 No. 3 (2026)

Open Access

Article

Article ID: 4266

Enhancing the validity of probability prompts using four generative artificial intelligence tools

by Ayşe Fethiye Gemci, Amanda Gantt Sawyer, Haley Smiley, Richard Busi, Juliana Galano

Computing and Artificial Intelligence, Vol.4, No.3, 2026;

Generative Artificial Intelligence (genAI) has become a large influence on students and teachers in the mathematics classroom. Specifically, researchers found that genAI tools could solve probability questions such as “What is the probability of drawing the Queen of Hearts from a standard deck of playing cards on the first try?” These researchers also stated that the genAI tools could not be trusted when solving, “Three cards are lying face down in front of me. Two are face cards, and there are no spades. No card is next to a card of the same color. What are the odds that the card on the left is the Queen of Hearts?” However, it is unclear whether that statement is true. Thus, we verified these findings by investigating four genAI tools (ChatGPT, Gemini, Co-Pilot, and Claude AI) on their ability to complete the identified probability task to determine when genAI can be trusted to perform probability calculation. Using the results of this investigation, we examined these genAI models using their responses and the predictions made by the genAI trainer. The investigation implies that developing mathematical validity requires intentional instruction, including teaching students how to compare genAI’s explanations, conducting GenAI Triangulation Process, cross-checking results using independent methods or alternative tools, and justifying whether an answer is mathematically sound.

Open Access

Article

Article ID: 4241

Image-Based Soil Nutrient Prediction Using Machine Learning and Deep Learning Models

by Preity Mishra, S.K. Chaulya, Vikash Kumar, Naresh Kumar, Abhinek Agarwal, Anant Stephen Besra

Computing and Artificial Intelligence, Vol.4, No.3, 2026;

Recent advances in precision agriculture have enabled the use of machine learning and image processing for rapid soil nutrient assessment. This study presents a non-destructive approach for estimating soil nutrient content from digital soil images captured using a camera and analyzed through specialized software. A dataset of 1,500 soil images was processed using color correction, noise reduction, and feature extraction techniques to improve data quality. Five machine learning models, Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Gradient Boosting Machine (GBM), and a Stacking Regressor, were evaluated for predicting nitrogen, phosphorus, and potassium levels. A Convolutional Neural Network (CNN) based deep learning model was served as a benchmark. No single model was best for every nutrient and metric. For nitrogen, GBM achieved the lowest test MAE (0.1652), whereas the Stacking Regressor achieved the highest test R² (0.9886). For potassium, Random Forest achieved the lowest test MAE (0.0051), whereas XGBoost achieved the highest test R² (0.9999). For phosphorus, XGBoost achieved both the lowest test MAE (0.1558) and the highest test R² (0.9929). Thus, the best-performing model depends on the nutrient and evaluation metric. Further validation using independent field datasets is required to establish robustness under diverse environmental conditions.